Using Machine Learning Techniques to Predict Viral Suppression Among People With HIV
Supervised ML algorithms may offer better performance for risk prediction of viral suppression than traditional statistical methods.
Authors
- Ruilie Cai (ORCID: https://orcid.org/0000-0003-2218-8000)
- Sharon Weissman (ORCID: https://orcid.org/0000-0002-2854-5745)
- Xueying Yang (ORCID: https://orcid.org/0000-0001-6788-0688)
- Bankole Olatosi (ORCID: https://orcid.org/0000-0002-8295-8735)
- Hao Helen Zhang (ORCID: https://orcid.org/0000-0001-8873-3329)
- Jiajia Zhang (ORCID: https://orcid.org/0000-0003-4566-0822)
- Xiaoming Li (ORCID: https://orcid.org/0000-0002-5555-9034)
- Xiaowen Sun (ORCID: https://orcid.org/0000-0002-6599-3686)
- Yunqing Ma (ORCID: https://orcid.org/0000-0002-0672-2237)
Institutions
- University of Arizona (US)
- University of South Carolina (US)
- Department of Health Services (US)
Publication Details
- Journal
- JAIDS Journal of Acquired Immune Deficiency Syndromes
- Published
- 2024-11-19
- DOI
- https://doi.org/10.1097/qai.0000000000003561
- Citations
- 1
- Primary Topic
- HIV/AIDS Research and Interventions
- Type
- article
- Field-Weighted Citation Impact
- 0.39